Turn Your Data into Actionable Insight
In today’s competitive business landscape, data is a key differentiator. Companies that can effectively leverage data can gain a competitive advantage and drive business success.
Your business is holding a treasure trove of valuable data. Tapping into this data can unleash valuable insights into your operations and provide you with the information you need to make strategic decisions that drive growth and innovation.
Maximizing Your Business Potential with Data-Driven Decisions
Data-driven decisions are crucial for companies of all sizes to stay competitive and grow. By gathering, analyzing, and utilizing data, businesses can gain insights into customer behavior, financial performance, and operational efficiency, which can inform decisions to improve operations and increase revenue.
Analytics is the tool that drives strategic business decisions to help you stay at the forefront of your competition.
Gone are the days when business decisions used to be made on gut feeling or by copying others. In this digital age, guesswork won’t work.
With the volume of data your business generates, you can no longer afford to be anything but data centric. Your data needs to be well-organized, accurate, and readily available.
Develop Your Data Core
Business expansion and innovation require a strong data strategy. Develop a data strategy that specifies how your data, people, processes, and technology should collaborate to achieve your business goals.
Data engineering provides vital business insights. You will have a deeper understanding of your clients, allowing you to make quicker, more informed business choices.
Even if you have all the knowledge in the world, poor communication will negate its value. As a result, custom analytics solutions and delivering those insights need to be well-designed to help you improve your business intelligence initiatives.
Realize the full potential of your stored data by removing data silos. Deploy BI solutions aimed to spur development and enhance decision-making.
Employing industry-leading toolkits, libraries, and frameworks will help your organization optimize predictive potential and expedite the time-to-value of its data assets.
Data visualization of KPIs makes it easy to spot trends, impact factors, and outliers. Turn your data into an interactive and personalized dashboard and connect the dots between the data of your company and the tales it tells.
Data Services Use Cases
Data Services Case Study
The client, a global asset management bank looking to identify and design enhancements for their risk management and performance reporting capabilities.
They were having challenges evaluating trends and patterns from disparate data sources and they also had a secondary focus on portfolio management and monitoring. They needed to implement three basic styles of data modeling:
After analyzing and modeling all the inputs that contributed to the calculation of the client’s portfolio Net Asset Value (NAV), this included corporate actions, fee accruals, future positions, and more. Additional data modeling actions were developed including:
- Identifying entity types
- Identifying attributes
- Applying naming conventions
- Identifying relationships
- Applying data model patterns
- High-level conceptualization
- Assigning keys
- Normalizing to reduce data redundancy
- De-normalizing to improve performance
These improvements enabled the Chief Information Officer to track performance drivers of various portfolios and evaluate risk metrics of investment themes.
Their new unique portfolio manager was re-designed for the client’s needs with the capabilities to forecast NAV, better manage cash reserves and monitor capital flow. You can read the full case study here.
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Data Services FAQs
What types of data can be analyzed with data analytics?
Data analytics can be applied to a wide range of data types, including structured data (such as numerical and categorical data) and unstructured data (such as text and image data).
Common types of data analyzed with data analytics include customer data, financial data, and operational data.
What are some common data analytics techniques?
Some common data analytics techniques include descriptive statistics, inferential statistics, data visualization, and machine learning.
Descriptive statistics are used to summarize and describe data, while inferential statistics are used to make predictions and infer relationships between variables.
Data visualization is used to create charts, graphs, and other visual representations of data to make it easier to understand and communicate.
Machine learning is a subfield of data analytics that involves using algorithms to learn from data and make predictions or decisions.
What tools are commonly used for data analytics?
Common data analytics tools include Excel, R, Python, and SQL.
Excel is a popular tool for working with structured data and performing basic data analysis tasks.
R and Python are open-source programming languages that are commonly used for data manipulation, visualization, and machine learning.
SQL is a programming language used for managing and querying relational databases.
What is the difference between data analytics and business intelligence?
Data analytics and business intelligence (BI) are closely related but have distinct differences.
Data analytics is the process of examining and interpreting data to extract useful insights and make informed decisions. BI, on the other hand, is the process of using data, technology, and tools to support decision-making and improve business performance.
How can data analytics be used to improve business performance?
Data analytics can be used to improve business performance in a variety of ways, such as:
- Identifying patterns and trends in customer data to improve marketing and sales strategies
- Analyzing financial data to identify areas of cost savings and improve financial performance
- Examining operational data to improve efficiency and productivity
- Using predictive modeling to forecast future trends and make better decisions
- Identifying and mitigating risks by analyzing data on potential threats and vulnerabilities.